Self-supervised monocular depth estimation methods aim to be used in critical
applications such as autonomous vehicles for environment analysis. To
circumvent the potential imperfections of these approaches, a quantification of
the prediction confidence is crucial to guide decision-making systems that rely
on depth estimation. In this paper, we propose MonoProb, a new unsupervised
monocular depth estimation method that returns an interpretable uncertainty,
which means that the uncertainty reflects the expected error of the network in
its depth predictions. We rethink the stereo or the structure-from-motion
paradigms used to train unsupervised monocular depth models as a probabilistic
problem. Within a single forward pass inference, this model provides a depth
prediction and a measure of its confidence, without increasing the inference
time. We then improve the performance on depth and uncertainty with a novel
self-distillation loss for which a student is supervised by a pseudo ground
truth that is a probability distribution on depth output by a teacher. To
quantify the performance of our models we design new metrics that, unlike
traditional ones, measure the absolute performance of uncertainty predictions.
Our experiments highlight enhancements achieved by our method on standard depth
and uncertainty metrics as well as on our tailored metrics.
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自我监督单目深度估计方法是用于自主车辆环境分析等关键应用的方法。该论文提出了 MonoProb，这是一种新的无监督单目深度估计方法，返回一个可解释的不确定性，该不确定性反映了网络在深度预测中的期望误差。通过引入一种新的自训练损失，可提高深度和不确定性的性能。实验结果表明了我们方法在标准深度和不确定性指标以及我们设计的指标上的改进。